Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding

TLDR: This paper presents one such formulation based on maximum likelihood estimation (MLE) of mixture models and the minimum description length (MDL) encoding principle of layered motion representation, and examines how many motion models adequately describe image motion.

Abstract: Representing and modeling the motion and spatial support of multiple objects and surfaces from motion video sequences is an important intermediate step towards dynamic image understanding. One such representation, called layered representation, has recently been proposed. Although a number of algorithms have been developed for computing these representations, there has not been a consolidated effort into developing a precise mathematical formulation of the problem. This paper presents one such formulation based on maximum likelihood estimation (MLE) of mixture models and the minimum description length (MDL) encoding principle. The three major issues in layered motion representation are: (i) how many motion models adequately describe image motion, (ii) what are the motion model parameters, and (iii) what is the spatial support layer for each motion model. >

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Frequently asked questions

Q1. What are the contributions in this paper?

In this paper, the authors present a survey of the state of the art in the area of online learning.